In many fields, people are requested to express their level of awareness about some risk (mainly associated with health, environment, energy, etc.) by selecting a category in an ordered scale.We propose a model for such ordinal data by taking into account that the observed response does not necessarily reflect the respondent’s true opinion since the final answer can be inaccurate or completely random. The proposed model hypothesizes three behaviors in the process of answering: accurate interviewees express their risk perception exactly, uncertain ones randomly select the response according to the uniform distribution, and inaccurate interviewees make evaluation errors but with high probability they choose a rating close to the true one. Statistical inference for the proposed models is addressed without assuming that the model, to be fitted, is correctly specified. Two real case studies on the awareness of geo-hydrological risk and work-related stress risk are considered using the proposed methodology.

Modelling different behaviors in disclosing risk perception

Giordano Sabrina
2020-01-01

Abstract

In many fields, people are requested to express their level of awareness about some risk (mainly associated with health, environment, energy, etc.) by selecting a category in an ordered scale.We propose a model for such ordinal data by taking into account that the observed response does not necessarily reflect the respondent’s true opinion since the final answer can be inaccurate or completely random. The proposed model hypothesizes three behaviors in the process of answering: accurate interviewees express their risk perception exactly, uncertain ones randomly select the response according to the uniform distribution, and inaccurate interviewees make evaluation errors but with high probability they choose a rating close to the true one. Statistical inference for the proposed models is addressed without assuming that the model, to be fitted, is correctly specified. Two real case studies on the awareness of geo-hydrological risk and work-related stress risk are considered using the proposed methodology.
2020
discrete distributions, geo-hydrological risk, misspecified models, mixture models, rating data, workrelated stress risk
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/302487
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